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Using Machine Learning to Predict if a Profiled Lay Rescuer can Successfully Deliver a Shock using a Public Access Automated External Defibrillator?

Bond, Raymond, Torney, Hannah, O’Hare, Peter, Davis, Laura, Delafont, Bruno, McReynolds, Hannah, McLister, Anna, McCartney, Ben, Di Maio, Rebecca, Finlay, Dewar, Guldenring, Daniel, McLaughlin, James and McEneaney, David (2017) Using Machine Learning to Predict if a Profiled Lay Rescuer can Successfully Deliver a Shock using a Public Access Automated External Defibrillator? In: Computing in Cardiology, Vancouver. IEEE. Vol 43 4 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/document/7868959/

DOI: 10.22489/CinC.2016. 1181-1184

Abstract

A public access automated external defibrillator (AED) is a device that is intended to be used by lay rescuers in an event where a member of the public experiences a sudden cardiac arrest due to a severe ventricular arrhythmia. Therefore, it is imperative that the human-machine interface of an AED is optimized in terms of its usability and intuitive design. This study involved the recruitment of 362 subjects (lay people) in a shopping mall to undertake the task of using an AED in a simulated environment as facilitated by a ‘sensorised’ manikin and an AED that was developed by HeartSine Technologies. We found that a large proportion (91.44%) of lay people can successfully use an AED in a simulated emergency scenario to deliver a successful shock. We also found that CPR training did not provide greater likelihood for shock success whilst those with AED training did. Exploratory data analysis and machine learning were used to determine if demographics and other variables are potential predictors for delivering a successful shock using an AED. We found that user demographics and educational attainment were not predictive for AED ‘usage’ success, which is reassuring since the objective of the medical industry is to develop AEDs that are intuitive to any member of the public.

Item Type:Conference contribution (Paper)
Keywords:AED, Automated External Defibrillator, machine learning, predictive modelling, human-machine systems, human computer interaction, health informatics
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Engineering
Faculty of Life and Health Sciences > School of Nursing
Faculty of Life and Health Sciences
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:37176
Deposited By: Dr Raymond Bond
Deposited On:16 Mar 2017 14:27
Last Modified:17 Oct 2017 16:28

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